• Training for Ocean Health Index regionaliziation: Canada, China, Israel, Baltic, S America

• Topic from local Meetups: R, Data Science

• 

• Why I love Github, R and RStudio

• Ocean Health Index
• Toolbox
• Github
• RStudio
• Data wrangling

• Is it pristine?

• "A healthy ocean sustainably delivers a range of benefits to people now and in the future."

• Recalculate OHI globally or regionally using alternative weights, equations, layers, etc.
• Regionalize based on administrative boundaries finer than EEZ.
• Visualize results to highlight best opportunities for improving ocean health.
• Interface with easy-to-use forms for sliding weights and concocting scenarios.
• Automate with tools for manipulating input layers and calculating OHI scores for sensitivity analyses.

Flower

Map

• examples
  • Baltic
  • Israel biogeographic and political
• globally
  • political: Global Administrative Areas (GADM)
  • biogeographic: Marine Ecoregions of the World (MEOW)
  • data:
    • pressures: extract from 1km Cumulative Impact rasters (Halpern et al 2008, Halpern et al in draft)
    • other: weight country values from ohi-global by area / coastal population / … of region
  • populate ohi-[country] scenario repository
  • deploy to ShinyApps.io for interactive website

• layers.csv, layers/
  • *.csv
• scenario.R, conf/
  • config.R
  • pressures_matrix.csv, resilience_matrix.csv, resilience_weights.csv
  • goals.csv
  • functions.R
• spatial/regions_gcs.js
• launchApp_code.R, launchApp.bat (Win), launchApp.command (Mac)
• scores.csv
• results/report.html, /figures

For example, calculate Baltic Health Index every year using scenarios bhi1980,..., bhi2014 as folders.

library(ohicore)

for (dir_scenario in sprintf('~/ohibaltic/bhi%d', 1980:2014)){
  setwd(dir_scenario)
  
  conf   = Conf('conf')
  layers = Layers('layers.csv', 'layers')
  scores = CalculateAll(conf, layers)
  
  write.csv(scores, 'scores.csv')
}

free, cross-platform, open source, web based:

• csv (comma-seperated value) data files. ancillary: md, json, shp, geotiff
  • Excel poor with Unicode, file locking. Try OpenOffice instead.
• R having libraries shiny web application, ggplot2 figures, dplyr data manipulation
  • RStudio excellent front end
• Github repositories:
  • backup to offsite archive, and rewind changes
  • document changes of code and files with issues and messages
  • collaborate with others and publish to web site

ohiprep | ohi-[scenario] | ohicore

1. Install R library: ohicore

library(devtools)
install_github('ohi-science/ohicore')

1. Collaborate on a scenario: eg ohi-global

• https://github.com/OHI-Science/ohi-global

• Download ZIP, Clone, Fork

• install script

where: * [org] is an organization (eg ohi-science) * [repo] is a repository in the orgranization (eg ohiprep) * [user] is your github username (eg bbest)

• track changes, issues, etc. free for public repos
• max: 1GB per repo, 100MB per file. so larger files (and binary) on file server, with remote vpn option
• render markdown, eg README.md

Track Changes View with "Rendered" button to view differences between versions of a text file: additions in green, removals in red strikethrough.

CSV View allows for on the fly tabular view, searching for text, and linking to specific rows of data.

Geographic View of GeoJSON renders automatically as a map.

• markdown is a plain text formatting syntax for conversion to HTML (with a tool)

• r markdown enables easy authoring of reproducible web reports from R

• in rstudio

• chunks: text, tables, figures

• inline: pi=`r pi` evaluates to "pi=3.1416"

• inline

The Arithmetic mean is equal to $\frac{1}{n} \sum_{i=i}^{n} x_{i}$, or the summation of n numbers divided by n.

The Arithmetic mean is equal to \(\frac{1}{n} \sum_{i=i}^{n} x_{i}\), or the summation of n numbers divided by n.

• chunked

$$
\frac{1}{n} \sum_{i=i}^{n} x_{i}
$$

\[ \frac{1}{n} \sum_{i=i}^{n} x_{i} \]

• post to rpubs from rstudio

  • this presentation http://rpubs.com/bdbest/rprod

  • more on Authoring R Presentations

• post to github

  • natively renders markdown (*.md)

  • easy to see change in simple text files (vs binary / proprietary formats)

RStudio: File > New Project > Version Control

• clone

• commit and push

• dplyr is the next iteration of plyr, focussed on tools for working with data frames.

• 

Calculate the batting average (AVG): number of base hits (H) divided by the total number of at bats (AB) using the Lahman baseball database. Limit to Babe Ruth and Jackie Robinson.

• setup

library(Lahman)
library(dplyr)
library(RSQLite)

• answer

nameFirst nameLast    avg
      Babe      Ruth  0.323
    Jackie  Robinson  0.308

• sql

tbl(lahman_sqlite(), sql(
"SELECT nameFirst, nameLast, 
  ROUND(AVG(H/(AB*1.0)), 3) AS avg 
FROM Batting
JOIN Master USING (playerID)
WHERE AB > 0 AND ((
  (nameFirst = 'Babe' AND 
   nameLast = 'Ruth') OR 
  (nameFirst = 'Jackie' AND 
   nameLast = 'Robinson')) 
GROUP BY nameFirst, nameLast
ORDER BY avg DESC")))

• chaining (%.%): grammar of data manipulation

Batting %.%
  merge(Master, by='playerID') %.%
  filter(
    AB > 0 &
    (nameFirst=='Babe' & 
     nameLast =='Ruth') | 
    (nameFirst=='Jackie' & 
     nameLast =='Robinson')) %.%  
  group_by(nameFirst, nameLast) %.%
  summarise(avg = round(mean(H/AB), 3)) %.%
  arrange(desc(avg))

• ohi-science.org
  • ohiprep: data layer preparation scripts
  • ohicore: R library of core functions
  • ohi-[scenario] for many scenarios: global, brazil, uswest, canada, israel, baltic…
• this talk as
  • html: http://bbest.github.io/talks/2014-06_OHI-repro-sci/
  • R markdown: http://github.com/bbest/bbest.github.com/tree/master/talks/2014-06_OHI-repro-sci/index.Rpres
• other
  • marwick reproducible research
  • github.com/datasets